Big data analysis of network metadata for optical networking systems

Optigo Networks正在通过重新定义智能物联网（IoT）的未来， 建筑物是相连的。通过解决建筑网络中的网络安全威胁，Optigo允许物联网 安全地扩展，实现使建筑物节能、舒适和安全的技术。 Optigo Networks为保护和连接当今最先进的建筑物提供了完整的解决方案。 Optigo的集成大数据分析和安全引擎位于强大的网络平台之上 确保建筑系统得到保护并无故障运行。 该研究项目将有助于推进Optigo Network的下一代分析引擎，该引擎使用网络 元数据以识别建筑物网络内的异常。使用流量速率、源和 目的地寻址、对等通信和功耗。该项目旨在使用机器 学习和其他统计和大数据工具，以实时检测甚至预测潜在问题 网络潜在的应用包括识别设备中的安全异常和故障。 我希望确定是否可以使用理论和实验物理学中常见的大数据技术 识别Optigo Networks产品线收集的数据中的异常情况。利用我们的数据专业知识 处理和建模，我们希望识别网络元数据中的模式和配置文件，Optigo 网络可以用来改进他们的异常检测工具。

Optigo Networks is shaping the future of the commercial Internet of Things (IoT) by redefining how smart buildings are connected. By addressing cybersecurity threats in the building network, Optigo allows the IoT to expand securely, enabling the technologies that make buildings energy efficient, comfortable and secure. Optigo Networks provides a complete solution to protect and connect today's most advanced buildings. Layered on top of a robust networking platform, Optigo's integrated big data analytics and security engine ensures that building systems are protected and operate trouble-free. This research project will help advance Optigo Network's next-gen analytics engine, which uses network metadata to identify anomalies within a building network. Using parameters such as traffic rates, source and destination addressing, peer communications and power consumption. This project aims to use machine learning and other statistical and big data tools to detect or even predict potential problems in a real-time network. Potential applications include identifying security anomalies and failures in devices. I wish to determine whether big data techniques common in theoretical and experimental physics can be used to identify anomalies in the data collected by Optigo Networks' product line. Using our expertise in data processing and modelling, we hope to identify patterns and profiles in the network metadata that Optigo Networks can use to advance their anomaly detection tools.